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A hybrid correction framework using disentangled seasonal-trend representations and MoE for NWP solar irradiance forecast

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Applied Energy 2025年 397卷

作者： Weijing Dou Kai Wang Shuo Shan Kanjian Zhang Haikun Wei Victor Sreeram State Grid Electric Power Research Institute (NARI Group Corporation) National Key Laboratory of Risk Defense Technology and Equipment for Power Grid Operation Nanjing 211106 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing 210096 China Southeast University Shenzhen Research Institute Shenzhen 518063 China Department of Electrical Electronics and Computer Engineering The University of Western Australia Perth Australia

Day-ahead solar irradiance forecast holds important value for optimizing energy utilization within the power system and ensuring stable grid scheduling. The forecast outputs of numerical weather prediction (NWP) are widely acknowledged as one of the indispensable data sources for day-ahead solar irradiance forecast tasks. In previous studies, post-processing methods have generally been employed as correction models to enhance the accuracy of NWP solar irradiance forecasts. However, irradiance sequences contain complex mixed patterns and exhibit various seasonal periodic differences. Based on the analysis of NWP global horizontal irradiance (GHI) error characteristics in this study, errors in NWP GHI forecasts also show obvious seasonal variations. Given these issues, it is challenging for a single correction model to achieve good correction performance and strong seasonal robustness. Therefore, this paper proposes a hybrid model comprising representation learning module, feature sparse activation module, and encoder-decoder-based correction module to address the aforementioned problems. A contrastive-learning-based representation learning module named CoST is introduced to learn disentangled seasonal features and trend features of irradiance sequences. A learnable mixture-of-experts (MoE) layer is adopted to sparsely activate the seasonal-trend features that contribute more to improving correction accuracy. The encoder-decoder-based correction module takes the sparsely activated seasonal-trend features as inputs, achieving the final corrected NWP GHI forecasts. The correction performance of the proposed method was validated on both publicly available datasets and actual field dataset. The results for various datasets show that our proposed CoST-MoELSTM model achieves the highest improvement for NWP forecasts, with increases of 29.82 %, 36.54 %, and 26.58 %. Additionally, we conducted a detailed analysis of the correction performance of CoST-MoELSTM across different

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A comparative study of eight human auditory models of monaural processing

arXiv

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arXiv 2021年

作者： Vecchi, Alejandro Osses Varnet, Léo Carney, Laurel H. Dau, Torsten Bruce, Ian C. Verhulst, Sarah Majdak, Piotr Laboratoire des systèmes perceptifs Département d'études cognitives École Normale Supérieure PSL University CNRS Paris France Departments of Biomedical Engineering and Neuroscience University of Rochester RochesterNY United States Hearing Systems Section Department of Health Technology Technical University of Denmark Lyngby Denmark Department of Electrical and Computer Engineering McMaster University HamiltonON Canada Hearing Technology group WAVES Department of Information Technology Ghent University Ghent Belgium Acoustics Research Institute Austrian Academy of Sciences Vienna Austria

A number of auditory models have been developed using diverging approaches, either physiological or perceptual, but they share comparable stages of signal processing, as they are inspired by the same constitutive parts of the auditory system. We compare eight monaural models that are openly accessible in the Auditory Modelling Toolbox. We discuss the considerations required to make the model outputs comparable to each other, as well as the results for the following model processing stages or their equivalents: Outer and middle ear, cochlear filter bank, inner hair cell, auditory nerve synapse, cochlear nucleus, and inferior colliculus. The discussion includes a list of recommendations for future applications of auditory models. © 2021, CC BY-NC-SA.

关键词： Physiological models

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Reconfigurable Intelligent Surfaces in Action for Non-Terrestrial Networks

arXiv

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arXiv 2020年

作者： Tekbıyık, Kürşat Kurt, Güneş Karabulut Ekti, Ali Rıza Yanikomeroglu, Halim The Department of Electronics and Communications Engineering Istanbul Technical University Istanbul Turkey The Department of Electrical Engineering Polytechnique Montréal Montréal Canada The Grid Communications and Security Group Electrification and Energy Infrastructure Division Oak Ridge National Laboratory Oak RidgeTN United States The Department of Systems and Computer Engineering Carleton University Ottawa Canada

Next-generation communication technology will be made possible by cooperation between terrestrial networks with non-terrestrial networks (NTN) comprised of high-altitude platform stations and satellites. Further, as humanity embarks on the long road to establish new habitats on other planets, the cooperation between NTN and deep-space networks (DSN) will be necessary. In this regard, we propose the use of reconfigurable intelligent surfaces (RIS) to improve coordination between these networks given that RIS perfectly match the size, weight, and power restrictions of operating in space. A comprehensive framework of RIS-assisted non-terrestrial and interplanetary communications is presented that pinpoints challenges, use cases, and open issues. Furthermore, the performance of RIS-assisted NTN under environmental effects such as solar scintillation and satellite drag is discussed in light of simulation results. © 2020, CC BY-NC-SA.

关键词： Satellites

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Performance analysis of SWIPT relaying systems in the presence of impulsive noise

arXiv

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arXiv 2020年

作者： Mohjazi, Lina Muhaidat, Sami Dianati, Mehrdad Al-Qutayri, Mahmoud Al-Dhahir, Naofal Center on Cyber-Physical Systems Khalifa University Abu Dhabi United Arab Emirates Preparatory Department Khalifa University Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Warwick Manufacturing Group University of Warwick CoventryCV4 7AL United Kingdom Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Electrical Engineering Department University of Texas at Dallas RichardsonTX75080 United States

We develop an analytical framework to characterize the effect of impulsive noise on the performance of relay-assisted simultaneous wireless information and power transfer (SWIPT) systems. We derive novel closed-form expressions for the pairwise error probability (PEP) considering two variants based on the availability of channel state information (CSI), namely, blind relaying and CSI-assisted relaying. We further consider two energy harvesting (EH) techniques, i.e., instantaneous EH (IEH) and average EH (AEH). Capitalizing on the derived analytical results, we present a detailed numerical investigation of the diversity order for the underlying scenarios under the impulsive noise assumption. For the case when two relays and the availability of a direct link, it is demonstrated that the considered SWIPT system with blind AEH-relaying is able to achieve an asymptotic diversity order of less than 3, which is equal to the diversity order achieved by CSI-assisted IEH-relaying. This result suggests that, by employing the blind AEH relaying, the power consumption of the network can be reduced, due to eliminating the need of CSI estimation. This can be achieved without any performance loss. Our results further show that placing the relays close to the source can significantly mitigate the detrimental effects of impulsive noise. Extensive Monte Carlo simulation results are presented to validate the accuracy of the proposed analytical framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy transfer

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Lookahead memory prefetching for cgras using partial loop unrolling 14th

Lookahead memory prefetching for cgras using partial loop un...

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14th International Symposium on Applied Reconfigurable Computing, ARC 2018

作者： Jung, Lukas Johannes Hochberger, Christian Department of Electrical Engineering and Information Technology Computer Systems Group TU Darmstadt Darmstadt Germany

ISBN: (纸本)9783319788890

Coarse Grained Reconfigurable Arrays have become an established approach to provide high computational performance in various environments. Several researchers have found that the achievable performance highly depends on the interface between memory and CGRA. In this contribution we show that a smart prefetching mechanism can increase the performance of the CGRA. At the same time it consumes less hardware resources and energy as state of the art prefetching mechanisms. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： computers

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From ancient times to modern: realizing the power of data visualization in healthcare and medicine

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Big Data Analytics 2020年第1期5卷 1-7页

作者： Elgendi, Mohamed Howard, Newton Hussain, Amir Menon, Carlo Ward, Rabab School of Electrical and Computer Engineering University of British Columbia Vancouver Canada Faculty of Medicine University of British Columbia Vancouver Canada School of Mechatronic Systems Engineering Simon Fraser University Burnaby Canada Nuffield Department of Surgical Sciences University of Oxford Oxford UK Data Science and Cyber Analytics Research Group Edinburgh Napier University Edinburgh UK

Data visuals (scientific images) display and express various amounts and types of information, and, as the saying goes,“an image is worth 1,000 words.” Based on a review of two studies, a new estimation of how many words an image is actually worth was calculated in an attempt to quantify the complicated biological process of image perception. The result revealed that an image is actually worth more than 30,000 words. This new value estimation provides insight into the power of images. Given that figures, graphs, and data visualizations are types of images commonly used in research and publications, every produced figure is important and must be carefully considered during the publication process.

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A reconfigurable quantum local area network over deployed fiber

arXiv

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arXiv 2021年

作者： Alshowkan, Muneer Williams, Brian P. Evans, Philip G. Rao, Nageswara S.V. Simmerman, Emma M. Lu, Hsuan-Hao Lingaraju, Navin B. Weiner, Andrew M. Marvinney, Claire E. Pai, Yun-Yi Lawrie, Benjamin J. Peters, Nicholas A. Lukens, Joseph M. Quantum Information Science Group Oak Ridge National Laboratory Oak RidgeTN37831 United States Autonomous and Complex Systems Group Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Applied Physics Stanford University StanfordCA94305 United States School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Materials Science and Technology Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

Practical quantum networking architectures are crucial for scaling the connection of quantum resources. Yet quantum network testbeds have thus far underutilized the full capabilities of modern lightwave communications, such as flexible-grid bandwidth allocation. In this work, we implement flex-grid entanglement distribution in a deployed network for the first time, connecting nodes in three distinct campus buildings time-synchronized via the Global Positioning System (GPS). We quantify the quality of the distributed polarization entanglement via log-negativity, which offers a generic metric of link performance in entangled bits per second. After demonstrating successful entanglement distribution for two allocations of our eight dynamically reconfigurable channels, we demonstrate remote state preparation—the first realization on deployed fiber—showcasing one possible quantum protocol enabled by the distributed entanglement network. Our results realize an advanced paradigm for managing entanglement resources in quantum networks of ever-increasing complexity and service demands. Copyright © 2021, The Authors. All rights reserved.

关键词： Quantum entanglement

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Data Needs and Challenges for Quantum Dot Devices Automation

arXiv

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arXiv 2023年

作者： Zwolak, Justyna P. Taylor, Jacob M. Andrews, Reed Benson, Jared Bryant, Garnett Buterakos, Donovan Chatterjee, Anasua Sarma, Sankar Das Eriksson, Mark A. Greplová, Eliška Gullans, Michael J. Hader, Fabian Kovach, Tyler J. Mundada, Pranav S. Ramsey, Mick Rasmussen, Torbjoern Severin, Brandon Sigillito, Anthony Undseth, Brennan Weber, Brian National Institute of Standards and Technology GaithersburgMD20899 United States Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD20742 United States Department of Physics University of Maryland College ParkMD20742 United States Joint Quantum Institute National Institute of Standards and Technology University of Maryland GaithersburgMD20899 United States HRL Laboratories LLC 3011 Malibu Canyon Road MalibuCA90265 United States Department of Physics University of Wisconsin-Madison MadisonWI53706 United States Center for Quantum Devices Niels Bohr Institute University of Copenhagen Copenhagen2100 Denmark Condensed Matter Theory Center University of Maryland College ParkMD20742 United States Kavli Institute of Nanoscience Delft University of Technology Lorentzweg 1 Delft2628 CJ Netherlands Central Institute of Engineering Electronics and Analytics ZEA-2 - Electronic Systems Forschungszentrum Jülich GmbH Jülich52425 Germany Q-CTRL Santa MonicaCA90401 United States Intel Components Research Intel Corporation HillsboroOR97124 United States Department of Materials University of Oxford Parks Road OxfordOX1 3PH United Kingdom School of Electrical Engineering and Telecommunications The University of New South Wales SydneyNSW2052 Australia Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States QuTech Delft University of Technology Lorentzweg 1 Delft2628 CJ Netherlands Intelligent Geometries LLC Lake Frederick VA22630 United States

Gate-defined quantum dots are a promising candidate system for realizing scalable, coupled qubit systems and serving as a fundamental building block for quantum computers. However, presentday quantum dot devices suffer from imperfections that must be accounted for, which hinders the characterization, tuning, and operation process. Moreover, with an increasing number of quantum dot qubits, the relevant parameter space grows sufficiently to make heuristic control infeasible. Thus, it is imperative that reliable and scalable autonomous tuning approaches are developed. This meeting report outlines current challenges in automating quantum dot device tuning and operation with a particular focus on datasets, benchmarking, and standardization. We also present insights and ideas put forward by the quantum dot community on how to overcome them. We aim to provide guidance and inspiration to researchers invested in automation efforts. Copyright © 2023, The Authors. All rights reserved.

关键词： Nanocrystals

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AI recognition of patient race in medical imaging: a modelling study

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The Lancet Digital Health 2022年第6期4卷 e406-e414页

作者： Gichoya, Judy Wawira Banerjee, Imon Bhimireddy, Ananth Reddy Burns, John L Celi, Leo Anthony Chen, Li-Ching Correa, Ramon Dullerud, Natalie Ghassemi, Marzyeh Huang, Shih-Cheng Kuo, Po-Chih Lungren, Matthew P Palmer, Lyle J Price, Brandon J Purkayastha, Saptarshi Pyrros, Ayis T Oakden-Rayner, Lauren Okechukwu, Chima Seyyed-Kalantari, Laleh Trivedi, Hari Wang, Ryan Zaiman, Zachary Zhang, Haoran Department of Radiology Emory University Atlanta GA United States Department of Computer Science Emory University Atlanta GA United States School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ United States School of Informatics and Computing Indiana University–Purdue University Indianapolis IN United States Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States Department of Medicine Beth Israel Deaconess Medical Center Boston MA United States Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science University of Toronto Toronto ON Canada Stanford University School of Medicine Palo Alto CA United States Australian Institute for Machine Learning University of Adelaide Adelaide SA Australia School of Public Health University of Adelaide Adelaide SA Australia Florida State University College of Medicine Tallahassee FL United States Dupage Medical Group Hinsdale IL United States Department of Computer Science Georgia Institute of Technology Atlanta GA United States Lunenfeld-Tanenbaum Research Institute Sinai Health Toronto ON Canada Vector Institute for Artificial Intelligence Toronto ON Canada

Background: Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Methods: Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race. Findings: In our study, we show that standard AI deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities, which was sustained under external validation conditions (x-ray imaging [area under the receiver operating characteristics curve (AUC) range 0·91–0·99], CT chest imaging [0·87–0·96], and mammography [0·81]). We also showed that this detection is not due to proxies or imaging-related surrogate covariates for race (eg, performance of possible confounders: body-mass index [AUC 0·55], disease distribution [0·61], and breast density [0·61]). Finally, we provide evidence to show that t

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RISCuer: A Reliable Multi-UAV Search and Rescue Testbed

arXiv

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arXiv 2020年

作者： Abdelkader, Mohamed Fiaz, Usman A. Toumi, Noureddine Mabrok, Mohamed A. Shamma, Jeff S. Lab King Abdullah University of Science & Technology Thuwal Saudi Arabia Department of Electrical & Computer Engineering Institute for Systems Research University of Maryland College ParkMD United States Group for Research in Decision Analysis Polytechnique Montreal QC Canada Department of Mathematics School of Engineering Australian College of Kuwait Kuwait

We present the Robotics Intelligent systems & Control (RISC) Lab multiagent testbed for reliable search and rescue and aerial transport in outdoor environments. The system consists of a team of three multirotor unmanned aerial vehicles (UAVs), which are capable of autonomously searching, picking up, and transporting randomly distributed objects in an outdoor field. The method involves vision based object detection and localization, passive aerial grasping with our novel design, GPS based UAV navigation, and safe release of the objects at the drop zone. Our cooperative strategy ensures safe spatial separation between UAVs at all times and we prevent any conflicts at the drop zone using communication enabled consensus. All computation is performed onboard each UAV. We describe the complete software and hardware architecture for the system and demonstrate its reliable performance using comprehensive outdoor experiments, and by comparing our results with some recent, similar works. Copyright © 2020, The Authors. All rights reserved.

关键词： Antennas

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